Study on ultrasonic quantitative evaluation technique based on BP neural network and D-S evidence theory

Ultrasonic detection technology is widely used because of its high sensitivity, strong penetrating ability, accurate defect location, simple operation, and harmlessness to the human body. However, it is still challenging to locate and quantify the defects whose shapes are complex based on ultrasonic testing. The amount of data required for ultrasonic imaging is relatively large, and the efficiency is relatively low. This paper proposes a new method that combines the BP neural network and D-S evidence theory fusion technology with the pulse reflection method. The circular and triangular defects are selected for numerical simulation and experimental testing. The diameter range of the circle is 1-5 mm. The base range of the isosceles triangle is 4-5 mm, and the height range is 3-5 mm. Finally, this paper researches the inversion imaging of single and multiple defects using neural networks, image processing technology and data fusion technology. The results show that after fusion, the similarity coefficient of defects can reach 0.96, and the minimum area error can reach 1.2 %. The maximum error of the average centroid x is only 9.25 %, and the minimum error is 6.36 %. The centroid y error is less than 12 %. The average centroid y error is only 8.73 % at the maximum and 5.09 % at the minimum, indicating that the defect-inversion is relatively accurate.